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研究生(外文):Hsiang-Ju Chiu
論文名稱(外文):Model establishment of predicting recurrent status of liver cancer patients using multiple measurements case-based reasoning method
指導教授(外文):Fei-Pei Lai
口試委員(外文):Jeng-Wei LinYu-Fang ChungZe-Xiong ChenKuo-Chin Huang
外文關鍵詞:Clinical datacase-based reasoning (CBR)multiple measurementscross-validationstandard deviation
  • 被引用被引用:0
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Due to the progress of medicine, clinical data are increased very rapidly and biochemistry laboratory items are multiply measured with the subsequent consultations of patients. These multiple measurements clinical data may become another problem during analysis. This study proposes a practicable method to appropriately handle the clinical data with multiple measurements. Based on the case-based reasoning (CBR) method, we propose a multiple measurements CBR (MMCBR) method, extended from single measurement CBR (SingleCBR), for analyzing clinical data. The research target of this study is the prediction of recurrent status of liver cancer patients after receiving the first treatment in one year. We randomly separated dataset into four subsets, and the average results of classification using three-fold cross validation in four random datasets are analyzed, respectively. The results show models with better performance in the mean accuracy of four random datasets. Combination CBR could produce comparable results with SingleCBR and might have better stability than that of SingleCBR according to the standard deviation of accuracy. The mean sensitivities of MMCBR and Combination CBR in most combinations are better than those of SingleCBR. In this study, five feature selection approaches, different time periods of clinical data merging, and different weights are examined for establishing a predictive model.

中文摘要 i
Chapter 1 Introduction 1
Chapter 2 Background and Related Work 4
2.1 Case-based reasoning 4
2.2 Case-based reasoning in the medical domain 4
2.3 Liver cancer related studies 6
Chapter 3 Method 8
3.1 Feature selection approaches 8
3.2 Case-based reasoning 10
3.3 Multiple measurements case-based reasoning (MMCBR) 11
3.3.1 The pair weight and case weight of MMCBR 12
3.4 Material 20
3.5 Evaluation 24
Chapter 4 Result 28
Chapter 5 Discussion 33
Chapter 6 Conclusion 35
Chapter 7 Future Work 36
Appendixes 37
A.1 Material 37
A.2 Evaluation 38
A.3 Result 39
A.4 Discussion 43
Reference 45

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